Semantic Web Usage Mining – Overview and Case Studies
author:
Bettina Berendt,
Humboldt University
Description
In this tutorial we will review fundamentals of web usage mining -
theory, case studies and related topics. Web usage mining is a topic
which became in the late 90ties one of the first profitable areas of
data mining and which was necessity for the succesful e-commerce
companies to understand better their customers, their behaviour
and to optimize the e-services accordingly. In this tutorial lecture
we will show several case studies which show approaches, techniques
and results coming out of this area.
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| Slides | |
| 0:00 | Semantic Web Usage Mining – Overview and Case Studies |
| 1:42 | Goals and top-level questions |
| 2:27 | Approaches to the current Web‘s biggest challenges: lots of data, human-understandable |
| 4:13 | Agenda |
| 4:23 | 1. What should I buy? |
| 4:44 | 2. Where do I find relevant information on ...? |
| 5:24 | 3. “What do people do there?“ |
| 6:04 | 4. How can a site be made usable – for a worldwide audience? |
| 6:44 | 5a. Why go to a shop ... |
| 7:01 | 5b. What is my site worth for my business? |
| 7:31 | 6. How to help people become active members of the knowledge society – help them to contribute content? |
| 8:42 | Agenda |
| 8:52 | Web Mining |
| 10:07 | Data analysis: the textbook version |
| 11:44 | Data analysis: the reality -> data mining / knowledge discovery process |
| 13:58 | Where does semantics come in? |
| 15:08 | Agenda |
| 15:42 | What is an ontology? |
| 17:32 | Agenda |
| 17:56 | Semantics of requests Step 1: Domain ontology |
| 22:41 | Semantics of requests Step 2: Modelling requests and sessions-as-sets |
| 24:59 | Semantics of sequences Step 3: Strategy pattern discovery |
| 26:13 | NB: For more exploratory analyses: The Web Usage Miner WUM |
| 26:20 | Semantics of sequences Step 4: Strategy pattern evaluation |
| 27:48 | Communication – Visual data mining Step 5: Mapping an ontological relation over concepts to a linear order and to visual variables |
| 28:35 | Ad Q.3: What do people do there? |
| 28:39 | Communication – Visual data mining Step 5 – Example |
| 32:49 | An online shop with a difference |
| 34:35 | Communication – Visual data mining Step 6: Visual abstraction new semantic patterns |
| 46:23 | Ad Q.4: Worldwide usability |
| 46:36 | The impact of language and domain knowledge on search option choice |
| 46:37 | Semantics: Service ontology |
| 46:38 | Results on frequent search patterns |
| 46:39 | Mining with ISOVIS: Semantic drill-down, visualizing detail & context |
| 46:47 | Ad Q.5: Shopping behaviour and Web site value |
| 46:56 | 5. What is my site worth for my business? |
| 50:32 | Semantics: The buying process as a service ontology |
| 50:38 | Mining (example): Association rules for investigating preferences in the buying process |
| 53:53 | Agenda |
| 54:10 | Step 6: Deployment of results Example 1: Using results for site improvement |
| 55:44 | Step 6: Deployment of results Example 2: Using results for personalization |
| 55:54 | Step 6: Deployment of results Example 3: A privacy-preserving Web-metrics analysis service |
| 60:08 | Agenda |
| 62:42 | Data and metadata in the Digital Library EDOC |
| 64:20 | Authoring support for document servers |
| 65:47 | … and this has consequences (problems of the fully manual approach) |
| 66:38 | The fully automatic approach |
| 67:08 | Why is this a problem? |
| 68:43 | The Scientific Authoring Process (1) |
| 69:43 | The Scientific Authoring Process (2) |
| 69:46 | IR-THESIS – System architecture |
| 70:17 | The Scientific Authoring Process (3) |
| 70:32 | Search and retrieval |
| 71:13 | The Scientific Authoring Process (4) |
| 71:31 | The Scientific Authoring Process (5) |
| 71:41 | Organisation of the literature /bibliography construction |
| 72:39 | The Scientific Authoring Process (6) |
| 72:45 | Discussion |
| 73:19 | The Scientific Authoring Process (7) |
| 73:33 | Writing |
| 80:34 | Conclusions and outlook |
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